In recent years, electronics in automobiles has been strikingly advanced, and a control of an automobile has come to be performed based on packet communication means such as Control Area Network (CAN). If a communications standard such as one described above is established, it becomes possible to obtain a large number of time series data which changes every moment from individual units of an automobile. Since these time series data are obtained as a result of observing operations of the automobile in detail, these data are expected to be applied to operation analysis, MC detection and the like.
Data obtained by CAN have the following characteristics:
1. The data includes an enormous amount of data. In some cases, data may be obtained for hundreds to thousands of variables.
2. In the data, there are contained control signals, and checksum variables used for error detection are mixed among physical variables whose values are set to be observed values such as revolutions and pressure.
3. Data are obtained at a high frequency at time intervals of 10 milliseconds.
As described above, data obtained by CAN are various and enormous. Accordingly, in many cases, an appropriate analysis cannot be performed in a conventional method where MC is found out by an engineer, visually checking a graphed data. On the other hand, inventors of the present invention proposed a technology for judging, on the basis of the same reference variable called a change-point, changing patterns of a plurality of variables whose kinds are different from one another (see T. Ide and K. Inoue: Knowledge Discovery from Heterogeneous Dynamic Systems using Change-point Correlations, in Proc. 2005 SIAM International Conference on Data Mining (SDM 05, Newport Beach, Calif., USA), Apr. 21-23, 2005). This technology makes MC judgment easy by automatically finding out a combination of variables associated with one another.